Setup

knitr::opts_chunk$set(echo = TRUE)

library(tidyverse)
library(R.utils)
library(wbCorr)
library(readxl)
library(kableExtra)
library(brms)
library(bayesplot)

top_directory <- file.path(
  'C:', 'Users', 'kueng', 'OneDrive - Universität Zürich UZH',
  '04 Papers', '02 T&T Control', 'Analysis', 'ACTIVITY', 'BRMS'
)

working_directory <- file.path(top_directory, 'SensitivityCovariates')
setwd(working_directory)

functions_directory <- file.path('C:', 'Users', 'kueng', 
                                 'OneDrive - Universität Zürich UZH', 
                                 'RFunctions')

source(file.path(functions_directory, 'ReportModels.R'))
source(file.path(functions_directory, 'PrettyTables.R'))
source(file.path(functions_directory, 'ReportMeasures.R'))
source(file.path(top_directory, 'Functions', 'PrepareData.R'))
system("shutdown /a")
## [1] 1116
# Set options for analysis
use_mi = FALSE
shutdown = FALSE
report_ordinal = FALSE

options(
  dplyr.print_max = 100, 
  brms.backend = 'cmdstan',
  brms.file_refit = ifelse(use_mi, 'never', 'on_change'),
  error = function() beepr::beep(sound = 5)
)
df <- openxlsx::read.xlsx(file.path(top_directory, 'long.xlsx'))
df_original <- df

df_double <- prepare_data(df, use_mi = use_mi)[[1]]

Constructing scales Re-coding pusing reshaping data (4field) centering data within and between

Modelling

# For indistinguishable Dyads
model_rows_fixed <- c(
    'Intercept', 
    # '-- WITHIN PERSON MAIN EFFECTS --', 
    'persuasion_self_cw', 
    'persuasion_partner_cw', 
    'pressure_self_cw', 
    'pressure_partner_cw', 
    'pushing_self_cw', 
    'pushing_partner_cw', 
    'day', 
    'weartime_self_cw',
    
    'support_self_cw',
    'support_partner_cw',
    'isWeekendWeekend',
    'got_JITAI_selfJITAIreceived',
    'skilled_supportDaysafterIntervention',
    
    
    # '-- BETWEEN PERSON MAIN EFFECTS',
    'persuasion_self_cb',
    'persuasion_partner_cb',
    'pressure_self_cb',
    'pressure_partner_cb',
    'pushing_self_cb',
    'pushing_partner_cb',
    'weartime_self_cb',
    'studyGroupFirst3weeksinterventions',
    'studyGrouplast3weeksinterventions'
  )


model_rows_fixed_ordinal <- c(
  model_rows_fixed[1],
  'Intercept[1]',
  'Intercept[2]',
  'Intercept[3]',
  'Intercept[4]',
  'Intercept[5]',
  model_rows_fixed[2:length(model_rows_fixed)]
)

model_rows_random <- c(
  # '--------------',
  # '-- RANDOM EFFECTS --',
  'sd(Intercept)', 
  'sd(persuasion_self_cw)',
  'sd(persuasion_partner_cw)',
  'sd(pressure_self_cw)',
  'sd(pressure_partner_cw)',
  'sd(pushing_self_cw)',
  'sd(pushing_partner_cw)',
  # '-- CORRELATION STRUCTURE -- ', 
  'ar[1]', 
  'nu',
  'shape',
  'sderr',
  'sigma'
)

model_rows_random_ordinal <- c(model_rows_random,'disc')
# For indistinguishable Dyads
model_rownames_fixed <- c(
    'Intercept', 
    # '-- WITHIN PERSON MAIN EFFECTS --', 
    'Daily perceived persuasion target -> target', 
    'Daily perceived persuasion target -> agent', 
    'Daily perceived pressure target -> target', 
    'Daily perceived pressure target -> agent', 
    'Daily perceived pushing target -> target', 
    'Daily perceived pushing target -> agent', 
    'Day', 
    'Daily weartime',
    
    'Daily perceived support target -> target',
    'Daily perceived support target -> agent',
    'Is a weekend',
    'JITAI received',
    'Days post skilled support intervention',
    
    # '-- BETWEEN PERSON MAIN EFFECTS',
    'Mean perceived persuasion target -> target',
    'Mean Perceived persuasion target -> agent',
    'Mean Perceived pressure target -> target',
    'Mean Perceived pressure target -> agent',
    'Mean Perceived pushing target -> target',
    'Mean Perceived pushing target -> agent',
    'Mean weartime',
    
    'Difference study group 2',
    'Difference study group 3'
  )


model_rownames_fixed_ordinal <- c(
  model_rownames_fixed[1],
  'Intercept[1]',
  'Intercept[2]',
  'Intercept[3]',
  'Intercept[4]',
  'Intercept[5]',
  model_rownames_fixed[2:length(model_rownames_fixed)]
)

model_rownames_random <- c(
  # '--------------',
  # '-- RANDOM EFFECTS --',
  'sd(Intercept)', 
  'sd(Daily perceived persuasion target -> target)', 
  'sd(Daily perceived persuasion target -> agent)', 
  'sd(Daily perceived pressure target -> target)', 
  'sd(Daily perceived pressure target -> agent)', 
  'sd(Daily perceived pushing target -> target)', 
  'sd(Daily perceived pushing target -> agent)', 
  # '-- CORRELATION STRUCTURE -- ', 
  'ar[1]', 
  'nu',
  'shape',
  'sderr',
  'sigma'
)

model_rownames_random_ordinal <- c(model_rownames_random,'disc')
rows_to_pack <- list(
  "Within-Person Effects" = c(2,14),
  "Between-Person Effects" = c(15,23),
  "Random Effects" = c(24, 30), 
  "Additional Parameters" = c(31,35)
  )

rows_to_pack_ordinal <- list(
  "Intercepts" = c(1,6),
  "Within-Person Effects" = c(2+5,14+5),
  "Between-Person Effects" = c(15+5,23+5),
  "Random Effects" = c(24+5, 30+5), 
  "Additional Parameters" = c(31+5,35+6)
  )

Subjective MVPA

range(df_double$pa_sub, na.rm = T) 
## [1]   0 720
hist(df_double$pa_sub, breaks = 100)

Modelling using the gaussian family fails. Due to the many zeros, transformations won’t help estimating the models. We employ the negative binomial family.

formula <- bf(
  pa_sub ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    #barriers_self_cw +
    
    #support_self_cw + support_partner_cw + 
    #comf_self_cw + reas_self_cw + 
    isWeekend + 
    got_JITAI_self + skilled_support +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    
    studyGroup +
    
    day + 
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID),

  autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)




prior1 <- c(
  brms::set_prior("normal(0, 2.5)", class = "b"),
  brms::set_prior("normal(0, 50)", class = "Intercept", lb = 0),
  
  brms::set_prior("normal(0, 10)", class = "sd", group = "coupleID", lb = 0),
  
  brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
  brms::set_prior("cauchy(0, 20)", class = "shape"), 
  brms::set_prior("cauchy(0, 10)", class='sderr')
)

#df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]

pa_sub <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = brms::negbinomial(),
  #control = list(adapt_delta = 0.99),
  iter = 5000,
  warmup = 2000,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path(working_directory, "models_cache", "NoExchangeProcesses_pa_sub")
)
pp_check(pa_sub, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

pp_check(pa_sub)
## Using 10 posterior draws for ppc type 'dens_overlay' by default.

loo(pa_sub)
## 
## Computed from 12000 by 3736 log-likelihood matrix.
## 
##          Estimate    SE
## elpd_loo -12058.6 177.6
## p_loo        31.5   2.9
## looic     24117.2 355.2
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.3, 1.8]).
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. ESS
## (-Inf, 0.7]   (good)     3730  99.8%   711     
##    (0.7, 1]   (bad)         6   0.2%   <NA>    
##    (1, Inf)   (very bad)    0   0.0%   <NA>    
## See help('pareto-k-diagnostic') for details.
plot(pa_sub, ask = FALSE)

summarize_brms(
  pa_sub, 
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = T) %>%
  print_df(rows_to_pack = rows_to_pack)
IRR l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 25.01* 16.31 38.47 1.001 4149.37 6899.00
Within-Person Effects
Daily perceived persuasion target -> target 1.20* 1.08 1.35 1.001 13815.80 8955.73
Daily perceived persuasion target -> agent 1.18* 1.06 1.33 1.000 13125.08 8597.14
Daily perceived pressure target -> target 0.93 0.70 1.28 1.001 10776.56 8213.55
Daily perceived pressure target -> agent 1.17 0.88 1.60 1.001 12278.39 8112.98
Daily perceived pushing target -> target 1.13 0.92 1.41 1.000 9634.92 7950.68
Daily perceived pushing target -> agent 1.14 0.97 1.36 1.000 13542.44 9570.66
Day 0.78 0.51 1.20 1.000 9287.04 9292.82
Daily weartime NA NA NA NA NA NA
Daily perceived support target -> target NA NA NA NA NA NA
Daily perceived support target -> agent NA NA NA NA NA NA
Is a weekend 1.26* 1.03 1.53 1.000 14997.04 8575.03
JITAI received 0.78 0.60 1.02 1.000 15416.78 8877.06
Days post skilled support intervention 1.07 0.77 1.47 1.000 9720.75 9233.75
Between-Person Effects
Mean perceived persuasion target -> target 1.64 0.84 3.21 1.000 3603.33 5293.90
Mean Perceived persuasion target -> agent 1.22 0.64 2.35 1.001 3737.59 5250.52
Mean Perceived pressure target -> target 0.50 0.23 1.09 1.001 5097.65 7541.12
Mean Perceived pressure target -> agent 0.44* 0.20 0.96 1.001 5325.89 7099.34
Mean Perceived pushing target -> target 1.69 0.63 4.56 1.001 4748.82 6354.96
Mean Perceived pushing target -> agent 1.84 0.68 5.27 1.000 4664.11 6870.92
Mean weartime NA NA NA NA NA NA
Difference study group 2 0.84 0.49 1.44 1.000 3262.86 5465.59
Difference study group 3 1.27 0.74 2.16 1.000 3826.49 6450.90
Random Effects
sd(Intercept) 0.61 0.45 0.83 1.00 4052.17 6511.73
sd(Daily perceived persuasion target -> target) 0.09 0.00 0.23 1.00 4880.75 4697.28
sd(Daily perceived persuasion target -> agent) 0.07 0.00 0.20 1.00 5781.38 5337.32
sd(Daily perceived pressure target -> target) 0.17 0.01 0.53 1.00 6966.79 6255.09
sd(Daily perceived pressure target -> agent) 0.16 0.01 0.48 1.00 7092.20 5410.81
sd(Daily perceived pushing target -> target) 0.28 0.02 0.58 1.00 3041.60 3386.28
sd(Daily perceived pushing target -> agent) 0.11 0.01 0.31 1.00 6199.16 5183.10
Additional Parameters
ar[1] 0.03 -0.94 0.94 1.00 11290.35 7161.12
nu NA NA NA NA NA NA
shape 0.14 0.13 0.14 1.00 16673.86 8356.42
sderr 0.05 0.00 0.14 1.00 6501.87 5199.07
sigma NA NA NA NA NA NA

Device Based MVPA

range(df_double$pa_obj, na.rm = T) 
## [1]   5.75 971.25
hist(df_double$pa_obj, breaks = 50)

df_double$pa_obj_log <- log(df_double$pa_obj)

hist(df_double$pa_obj_log, breaks = 50)

We tried negative binomial here as well for consistency, but the model did not converge. Poisson also did not work. As we have no zeros in this distribution, we log transform.

formula <- bf(
  pa_obj_log ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    #barriers_self_cw +
    
    #support_self_cw + support_partner_cw + 
    #comf_self_cw + reas_self_cw + 
    isWeekend + 
    got_JITAI_self + skilled_support +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    
    studyGroup +
    
    day + weartime_self_cw + weartime_self_cb +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID),

  autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)



prior1 <- c(
  brms::set_prior("normal(0, 2.5)", class = "b"),
  brms::set_prior("normal(0, 50)", class = "Intercept", lb = 0),
  
  brms::set_prior("normal(0, 10)", class = "sd", group = "coupleID", lb = 0),
  
  brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
  brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
)


#df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]

pa_obj_log <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = gaussian(),
  #control = list(adapt_delta = 0.99),
  iter = 5000,
  warmup = 2000,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path(working_directory,"models_cache", "NoExchangeProcesses_pa_obj_log")
)
# plotting with the first imputed dataset. 
pp_check(pa_obj_log, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

pp_check(pa_obj_log)
## Using 10 posterior draws for ppc type 'dens_overlay' by default.

loo(pa_obj_log)
## 
## Computed from 12000 by 3337 log-likelihood matrix.
## 
##          Estimate    SE
## elpd_loo  -2810.4  56.8
## p_loo        94.7   4.7
## looic      5620.8 113.5
## ------
## MCSE of elpd_loo is 0.1.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.3, 1.9]).
## 
## All Pareto k estimates are good (k < 0.7).
## See help('pareto-k-diagnostic') for details.
plot(pa_obj_log, ask = FALSE)

summarize_brms(
  pa_obj_log, 
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = T) %>%
  print_df(rows_to_pack = rows_to_pack)
exp(Est.) l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 120.79* 100.83 145.18 1.000 4239.20 6209.51
Within-Person Effects
Daily perceived persuasion target -> target 1.03 1.00 1.05 1.001 12390.20 10544.56
Daily perceived persuasion target -> agent 1.01 0.99 1.04 1.000 14962.92 9550.28
Daily perceived pressure target -> target 0.95 0.89 1.01 1.000 19051.70 9387.28
Daily perceived pressure target -> agent 0.98 0.92 1.04 1.000 20150.84 9560.53
Daily perceived pushing target -> target 1.03 0.98 1.08 1.000 15472.49 8561.84
Daily perceived pushing target -> agent 1.03 0.99 1.07 1.000 18983.33 9512.96
Day 0.94 0.83 1.05 1.000 17456.49 9555.89
Daily weartime 1.00* 1.00 1.00 1.000 11809.03 7727.81
Daily perceived support target -> target NA NA NA NA NA NA
Daily perceived support target -> agent NA NA NA NA NA NA
Is a weekend 1.06* 1.02 1.11 1.001 30803.57 8741.86
JITAI received 0.93* 0.88 0.98 1.001 29224.44 8422.62
Days post skilled support intervention 1.04 0.96 1.14 1.000 17346.08 10340.33
Between-Person Effects
Mean perceived persuasion target -> target 1.07 0.79 1.45 1.001 3976.33 5874.57
Mean Perceived persuasion target -> agent 0.95 0.70 1.29 1.001 3841.11 5642.70
Mean Perceived pressure target -> target 0.98 0.70 1.37 1.001 5542.79 7814.95
Mean Perceived pressure target -> agent 1.00 0.73 1.38 1.001 5235.22 7668.65
Mean Perceived pushing target -> target 1.03 0.66 1.61 1.000 5493.05 7623.64
Mean Perceived pushing target -> agent 1.30 0.83 2.03 1.000 5534.05 7234.52
Mean weartime 1.00 1.00 1.00 1.000 17389.53 9548.63
Difference study group 2 0.89 0.69 1.15 1.000 3542.44 5972.18
Difference study group 3 0.98 0.75 1.26 1.001 4327.24 6229.46
Random Effects
sd(Intercept) 0.30 0.23 0.40 1.00 3530.98 5797.86
sd(Daily perceived persuasion target -> target) 0.05 0.03 0.08 1.00 6646.03 5917.60
sd(Daily perceived persuasion target -> agent) 0.05 0.02 0.08 1.00 5928.57 5562.21
sd(Daily perceived pressure target -> target) 0.05 0.00 0.14 1.00 6617.04 7769.32
sd(Daily perceived pressure target -> agent) 0.04 0.00 0.11 1.00 7848.04 7410.27
sd(Daily perceived pushing target -> target) 0.06 0.00 0.15 1.00 2831.10 4983.82
sd(Daily perceived pushing target -> agent) 0.03 0.00 0.08 1.00 6349.87 6731.72
Additional Parameters
ar[1] 0.30 0.26 0.33 1.00 27789.17 9023.27
nu NA NA NA NA NA NA
shape NA NA NA NA NA NA
sderr NA NA NA NA NA NA
sigma 0.55 0.54 0.57 1.00 25699.80 8389.43

Affect

range(df_double$aff, na.rm = T) 
## [1] 1 6
hist(df_double$aff, breaks = 15)

formula <- bf(
  aff ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    #barriers_self_cw +
    
    #support_self_cw + support_partner_cw + 
    #comf_self_cw + reas_self_cw + 
    isWeekend + 
    got_JITAI_self + skilled_support +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    
    studyGroup +
    
    day +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID),

  autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)



prior1 <- c(
  brms::set_prior("normal(0, 5)", class = "b"),
  brms::set_prior("normal(0, 20)", class = "Intercept", lb=0, ub=6), # range of the outcome scale
  
  brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0),

  brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
  brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
  
)


df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]

mood <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = gaussian(),
  #control = list(adapt_delta = 0.95, max_treedepth = 15),
  iter = 5000,
  warmup = 2000,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path(working_directory,"models_cache", "NoExchangeProcesses_mood")
)
pp_check(mood, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

pp_check(mood)
## Using 10 posterior draws for ppc type 'dens_overlay' by default.

loo(mood)
## 
## Computed from 12000 by 3736 log-likelihood matrix.
## 
##          Estimate    SE
## elpd_loo  -4811.7  63.8
## p_loo        92.9   4.7
## looic      9623.3 127.6
## ------
## MCSE of elpd_loo is 0.1.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.5, 1.5]).
## 
## All Pareto k estimates are good (k < 0.7).
## See help('pareto-k-diagnostic') for details.
plot(mood, ask = FALSE)

summarize_brms(
  mood, 
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = F) %>%
  print_df(rows_to_pack = rows_to_pack)
b l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 4.64* 4.31 4.97 1.001 5416.97 7206.82
Within-Person Effects
Daily perceived persuasion target -> target 0.00 -0.03 0.03 1.000 17624.62 9084.58
Daily perceived persuasion target -> agent 0.01 -0.03 0.05 1.000 18226.01 10360.31
Daily perceived pressure target -> target -0.05 -0.17 0.05 1.000 14620.65 9742.35
Daily perceived pressure target -> agent -0.04 -0.18 0.09 1.000 12496.42 9495.99
Daily perceived pushing target -> target 0.02 -0.04 0.09 1.000 17337.89 9901.67
Daily perceived pushing target -> agent 0.06* 0.01 0.12 1.000 17695.63 9700.51
Day 0.21 -0.01 0.43 1.000 20666.67 9041.60
Daily weartime NA NA NA NA NA NA
Daily perceived support target -> target NA NA NA NA NA NA
Daily perceived support target -> agent NA NA NA NA NA NA
Is a weekend 0.12* 0.05 0.18 1.000 27503.98 9111.39
JITAI received -0.09* -0.16 -0.01 1.001 29559.03 8712.12
Days post skilled support intervention 0.04 -0.11 0.19 1.000 18426.54 8590.84
Between-Person Effects
Mean perceived persuasion target -> target 0.39 -0.18 0.94 1.001 4320.62 6530.57
Mean Perceived persuasion target -> agent 0.30 -0.26 0.86 1.001 3943.96 6447.24
Mean Perceived pressure target -> target -0.30 -0.88 0.27 1.000 5611.55 8288.79
Mean Perceived pressure target -> agent -0.31 -0.87 0.26 1.000 5342.63 7661.31
Mean Perceived pushing target -> target 0.16 -0.61 0.92 1.001 6877.67 8256.86
Mean Perceived pushing target -> agent 0.30 -0.46 1.07 1.001 6644.11 8503.66
Mean weartime NA NA NA NA NA NA
Difference study group 2 -0.23 -0.67 0.21 1.000 5355.71 7115.82
Difference study group 3 0.37 -0.10 0.82 1.001 5077.59 6871.62
Random Effects
sd(Intercept) 0.55 0.42 0.72 1.00 5169.42 7380.36
sd(Daily perceived persuasion target -> target) 0.03 0.00 0.07 1.00 6266.44 6879.82
sd(Daily perceived persuasion target -> agent) 0.06 0.01 0.11 1.00 4029.33 5092.95
sd(Daily perceived pressure target -> target) 0.12 0.01 0.29 1.00 4182.12 6220.82
sd(Daily perceived pressure target -> agent) 0.18 0.02 0.37 1.00 4486.37 4469.96
sd(Daily perceived pushing target -> target) 0.09 0.01 0.17 1.00 5658.16 5434.46
sd(Daily perceived pushing target -> agent) 0.05 0.00 0.13 1.00 5847.72 6812.15
Additional Parameters
ar[1] 0.45 0.42 0.48 1.00 20615.58 8440.25
nu NA NA NA NA NA NA
shape NA NA NA NA NA NA
sderr NA NA NA NA NA NA
sigma 0.87 0.85 0.89 1.00 21994.40 7645.11

reactance

range(df_double$reactance, na.rm = T) 
## [1] 0 5
hist(df_double$reactance, breaks = 6)

formula <- bf(
  reactance ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    #barriers_self_cw +
    
    #support_self_cw + support_partner_cw + 
    #comf_self_cw + reas_self_cw + 
    isWeekend + 
    got_JITAI_self + skilled_support +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    
    studyGroup +
    
    day +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID),

  autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)



prior1 <- c(
  brms::set_prior("normal(0, 5)", class = "b"),
  brms::set_prior("normal(0, 20)", class = "Intercept", lb=0, ub=5), # range of the outcome scale
  
  brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0),

  brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1),
  brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
)


df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]

reactance <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = gaussian(),
  #control = list(adapt_delta = 0.95, max_treedepth = 15),
  iter = 5000,
  warmup = 2000,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path(working_directory,"models_cache", "NoExchangeProcesses_reactance")

)
pp_check(reactance, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

pp_check(reactance)
## Using 10 posterior draws for ppc type 'dens_overlay' by default.

loo(reactance)
## 
## Computed from 12000 by 756 log-likelihood matrix.
## 
##          Estimate   SE
## elpd_loo  -1060.4 35.5
## p_loo        75.5  7.4
## looic      2120.8 70.9
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.5, 1.5]).
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. ESS
## (-Inf, 0.7]   (good)     747   98.8%   132     
##    (0.7, 1]   (bad)        9    1.2%   <NA>    
##    (1, Inf)   (very bad)   0    0.0%   <NA>    
## See help('pareto-k-diagnostic') for details.
plot(reactance, ask = FALSE)

summary(reactance)
##  Family: gaussian 
##   Links: mu = identity; sigma = identity 
## Formula: reactance ~ persuasion_self_cw + persuasion_partner_cw + pressure_self_cw + pressure_partner_cw + pushing_self_cw + pushing_partner_cw + isWeekend + got_JITAI_self + skilled_support + persuasion_self_cb + persuasion_partner_cb + pressure_self_cb + pressure_partner_cb + pushing_self_cb + pushing_partner_cb + studyGroup + day + (persuasion_self_cw + persuasion_partner_cw + pressure_self_cw + pressure_partner_cw + pushing_self_cw + pushing_partner_cw | coupleID) 
##          autocor ~ ar(time = day, gr = coupleID:userID, p = 1)
##    Data: data (Number of observations: 756) 
##   Draws: 4 chains, each with iter = 5000; warmup = 2000; thin = 1;
##          total post-warmup draws = 12000
## 
## Correlation Structures:
##       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## ar[1]     0.01      0.04    -0.08     0.09 1.00    14667     9441
## 
## Multilevel Hyperparameters:
## ~coupleID (Number of levels: 38) 
##                                                Estimate Est.Error l-95% CI u-95% CI Rhat
## sd(Intercept)                                      0.19      0.08     0.04     0.36 1.00
## sd(persuasion_self_cw)                             0.04      0.03     0.00     0.11 1.00
## sd(persuasion_partner_cw)                          0.05      0.04     0.00     0.13 1.00
## sd(pressure_self_cw)                               0.40      0.10     0.23     0.62 1.00
## sd(pressure_partner_cw)                            0.22      0.16     0.01     0.58 1.00
## sd(pushing_self_cw)                                0.12      0.06     0.01     0.24 1.00
## sd(pushing_partner_cw)                             0.05      0.04     0.00     0.15 1.00
## cor(Intercept,persuasion_self_cw)                 -0.21      0.35    -0.80     0.53 1.00
## cor(Intercept,persuasion_partner_cw)               0.08      0.34    -0.61     0.70 1.00
## cor(persuasion_self_cw,persuasion_partner_cw)     -0.03      0.35    -0.68     0.65 1.00
## cor(Intercept,pressure_self_cw)                   -0.00      0.30    -0.58     0.57 1.00
## cor(persuasion_self_cw,pressure_self_cw)           0.02      0.34    -0.64     0.66 1.00
## cor(persuasion_partner_cw,pressure_self_cw)        0.00      0.34    -0.65     0.65 1.00
## cor(Intercept,pressure_partner_cw)                 0.07      0.33    -0.57     0.68 1.00
## cor(persuasion_self_cw,pressure_partner_cw)        0.05      0.35    -0.64     0.68 1.00
## cor(persuasion_partner_cw,pressure_partner_cw)     0.00      0.35    -0.66     0.67 1.00
## cor(pressure_self_cw,pressure_partner_cw)         -0.18      0.32    -0.72     0.51 1.00
## cor(Intercept,pushing_self_cw)                     0.18      0.31    -0.46     0.74 1.00
## cor(persuasion_self_cw,pushing_self_cw)           -0.02      0.34    -0.66     0.64 1.00
## cor(persuasion_partner_cw,pushing_self_cw)         0.11      0.35    -0.58     0.73 1.00
## cor(pressure_self_cw,pushing_self_cw)             -0.12      0.31    -0.67     0.50 1.00
## cor(pressure_partner_cw,pushing_self_cw)           0.08      0.36    -0.62     0.72 1.00
## cor(Intercept,pushing_partner_cw)                  0.05      0.35    -0.62     0.70 1.00
## cor(persuasion_self_cw,pushing_partner_cw)         0.02      0.36    -0.66     0.69 1.00
## cor(persuasion_partner_cw,pushing_partner_cw)     -0.03      0.36    -0.69     0.65 1.00
## cor(pressure_self_cw,pushing_partner_cw)          -0.05      0.35    -0.70     0.63 1.00
## cor(pressure_partner_cw,pushing_partner_cw)        0.03      0.35    -0.64     0.69 1.00
## cor(pushing_self_cw,pushing_partner_cw)            0.04      0.35    -0.64     0.69 1.00
##                                                Bulk_ESS Tail_ESS
## sd(Intercept)                                      3431     2862
## sd(persuasion_self_cw)                             3634     5167
## sd(persuasion_partner_cw)                          5652     5644
## sd(pressure_self_cw)                               6502     8218
## sd(pressure_partner_cw)                            3087     6599
## sd(pushing_self_cw)                                3834     4180
## sd(pushing_partner_cw)                             7144     6970
## cor(Intercept,persuasion_self_cw)                 10285     9083
## cor(Intercept,persuasion_partner_cw)              16889     9733
## cor(persuasion_self_cw,persuasion_partner_cw)     13998     9771
## cor(Intercept,pressure_self_cw)                    4275     6770
## cor(persuasion_self_cw,pressure_self_cw)           3297     6220
## cor(persuasion_partner_cw,pressure_self_cw)        3525     7020
## cor(Intercept,pressure_partner_cw)                 9938     8724
## cor(persuasion_self_cw,pressure_partner_cw)        8763     9564
## cor(persuasion_partner_cw,pressure_partner_cw)     8508     8756
## cor(pressure_self_cw,pressure_partner_cw)         11488     9793
## cor(Intercept,pushing_self_cw)                     7158     7475
## cor(persuasion_self_cw,pushing_self_cw)            7112     8016
## cor(persuasion_partner_cw,pushing_self_cw)         6696     9638
## cor(pressure_self_cw,pushing_self_cw)              9833     9525
## cor(pressure_partner_cw,pushing_self_cw)           6808     9599
## cor(Intercept,pushing_partner_cw)                 19919     9019
## cor(persuasion_self_cw,pushing_partner_cw)        16633     9193
## cor(persuasion_partner_cw,pushing_partner_cw)     12144     9249
## cor(pressure_self_cw,pushing_partner_cw)          14789    10120
## cor(pressure_partner_cw,pushing_partner_cw)       10499    11271
## cor(pushing_self_cw,pushing_partner_cw)           11841    10281
## 
## Regression Coefficients:
##                                      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## Intercept                                0.67      0.13     0.42     0.91 1.00    12391
## persuasion_self_cw                      -0.05      0.03    -0.10     0.01 1.00    16431
## persuasion_partner_cw                    0.00      0.03    -0.06     0.07 1.00    14098
## pressure_self_cw                         0.25      0.11     0.03     0.47 1.00     8878
## pressure_partner_cw                      0.14      0.11    -0.06     0.37 1.00     8820
## pushing_self_cw                          0.09      0.04     0.01     0.18 1.00    11133
## pushing_partner_cw                      -0.01      0.04    -0.10     0.08 1.00    15988
## isWeekendWeekend                        -0.14      0.08    -0.30     0.01 1.00    20346
## got_JITAI_selfJITAIreceived              0.00      0.11    -0.20     0.22 1.00    16729
## skilled_supportDaysafterIntervention    -0.10      0.13    -0.36     0.15 1.00    11974
## persuasion_self_cb                       0.05      0.17    -0.29     0.39 1.00     9115
## persuasion_partner_cb                    0.10      0.19    -0.28     0.48 1.00     9206
## pressure_self_cb                         0.60      0.20     0.22     0.99 1.00    11067
## pressure_partner_cb                      0.19      0.21    -0.23     0.60 1.00     9480
## pushing_self_cb                         -0.20      0.26    -0.70     0.30 1.00    10403
## pushing_partner_cb                      -0.54      0.27    -1.08     0.00 1.00    12417
## studyGroupFirst3weeksinterventions       0.08      0.13    -0.19     0.34 1.00    10558
## studyGrouplast3weeksinterventions       -0.32      0.14    -0.59    -0.04 1.00     9283
## day                                      0.16      0.19    -0.20     0.53 1.00    12724
##                                      Tail_ESS
## Intercept                                9622
## persuasion_self_cw                       9721
## persuasion_partner_cw                    9325
## pressure_self_cw                         8057
## pressure_partner_cw                      8116
## pushing_self_cw                          9362
## pushing_partner_cw                      10163
## isWeekendWeekend                         8422
## got_JITAI_selfJITAIreceived              9378
## skilled_supportDaysafterIntervention     9353
## persuasion_self_cb                       9242
## persuasion_partner_cb                    9564
## pressure_self_cb                         9102
## pressure_partner_cb                      8465
## pushing_self_cb                          9198
## pushing_partner_cb                       9838
## studyGroupFirst3weeksinterventions       9458
## studyGrouplast3weeksinterventions        8654
## day                                      8502
## 
## Further Distributional Parameters:
##       Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma     0.93      0.03     0.88     0.98 1.00    12595     8490
## 
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).
summarize_brms(
  reactance, 
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = F) %>%
  print_df(rows_to_pack = rows_to_pack)
b l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 0.67* 0.42 0.91 1.000 12391.46 9621.83
Within-Person Effects
Daily perceived persuasion target -> target -0.05 -0.10 0.01 1.000 16431.02 9720.80
Daily perceived persuasion target -> agent 0.00 -0.06 0.07 1.001 14097.52 9324.77
Daily perceived pressure target -> target 0.25* 0.03 0.47 1.000 8878.47 8057.12
Daily perceived pressure target -> agent 0.14 -0.06 0.37 1.000 8819.52 8115.90
Daily perceived pushing target -> target 0.09* 0.01 0.18 1.000 11132.58 9362.05
Daily perceived pushing target -> agent -0.01 -0.10 0.08 1.000 15987.84 10163.24
Day 0.16 -0.20 0.53 1.001 12724.02 8501.67
Daily weartime NA NA NA NA NA NA
Daily perceived support target -> target NA NA NA NA NA NA
Daily perceived support target -> agent NA NA NA NA NA NA
Is a weekend -0.14 -0.30 0.01 1.001 20345.67 8422.39
JITAI received 0.00 -0.20 0.22 1.000 16728.88 9377.54
Days post skilled support intervention -0.10 -0.36 0.15 1.000 11973.55 9353.34
Between-Person Effects
Mean perceived persuasion target -> target 0.05 -0.29 0.39 1.001 9114.74 9242.49
Mean Perceived persuasion target -> agent 0.10 -0.28 0.48 1.001 9206.41 9563.53
Mean Perceived pressure target -> target 0.60* 0.22 0.99 1.000 11067.40 9102.09
Mean Perceived pressure target -> agent 0.19 -0.23 0.60 1.001 9479.52 8465.23
Mean Perceived pushing target -> target -0.20 -0.70 0.30 1.000 10402.72 9197.52
Mean Perceived pushing target -> agent -0.54 -1.08 0.00 1.000 12417.03 9838.03
Mean weartime NA NA NA NA NA NA
Difference study group 2 0.08 -0.19 0.34 1.001 10557.83 9457.88
Difference study group 3 -0.32* -0.59 -0.04 1.000 9282.86 8654.37
Random Effects
sd(Intercept) 0.19 0.04 0.36 1.00 3431.34 2861.82
sd(Daily perceived persuasion target -> target) 0.04 0.00 0.11 1.00 3634.34 5167.11
sd(Daily perceived persuasion target -> agent) 0.05 0.00 0.13 1.00 5652.48 5644.07
sd(Daily perceived pressure target -> target) 0.40 0.23 0.62 1.00 6501.66 8218.15
sd(Daily perceived pressure target -> agent) 0.22 0.01 0.58 1.00 3086.62 6599.12
sd(Daily perceived pushing target -> target) 0.12 0.01 0.24 1.00 3834.07 4179.69
sd(Daily perceived pushing target -> agent) 0.05 0.00 0.15 1.00 7144.19 6969.82
Additional Parameters
ar[1] 0.01 -0.08 0.09 1.00 14667.35 9441.44
nu NA NA NA NA NA NA
shape NA NA NA NA NA NA
sderr NA NA NA NA NA NA
sigma 0.93 0.88 0.98 1.00 12594.89 8490.33

Binary Version

introduce_binary_reactance <- function(data) {
  data$is_reactance <- factor(data$reactance > 0, levels = c(FALSE, TRUE), labels = c(0, 1))
  return(data)
}



df_double <- introduce_binary_reactance(df_double)
if (use_mi) {
  for (i in seq_along(implist)) {
    implist[[i]] <- introduce_binary_reactance(implist[[i]])
  }
}


formula <- bf(
  is_reactance ~ 
    persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw +
    #barriers_self_cw +
    
    #support_self_cw + support_partner_cw + support_partner_cw +
    #comf_self_cw + reas_self_cw + 
    isWeekend + 
    got_JITAI_self + skilled_support +
    
    persuasion_self_cb + persuasion_partner_cb +
    pressure_self_cb + pressure_partner_cb +
    pushing_self_cb + pushing_partner_cb +
    
    
    studyGroup +
    
    day +
    
    # Random effects
    (persuasion_self_cw + persuasion_partner_cw +
    pressure_self_cw + pressure_partner_cw +
    pushing_self_cw + pushing_partner_cw | coupleID),

  autocor = ~ ar(time = day, gr = coupleID:userID, p = 1)
)



prior1 <- c(
  brms::set_prior("normal(0, 5)", class = "b"),
  brms::set_prior("normal(0, 20)", class = "Intercept", lb=0, ub=5), # range of the outcome scale
  
  brms::set_prior("normal(0, 2)", class = "sd", group = "coupleID", lb = 0),

  brms::set_prior("cauchy(0, 5)", class = "ar", lb = -1, ub = 1)
  #brms::set_prior("cauchy(0, 10)", class = "sigma", lb = 0)
)


df_minimal <- df_double[, c("userID", all.vars(as.formula(formula)))]

is_reactance <- my_brm(
  mi = use_mi, 
  imputed_data = implist,
  
  formula = formula, 
  prior = prior1,
  data = df_double, 
  family = bernoulli(),
  #control = list(adapt_delta = 0.95, max_treedepth = 15),
  iter = 5000,
  warmup = 2000,
  chains = 4,
  cores = 4,
  seed = 7777,
  file = file.path(working_directory,"models_cache", "NoExchangeProcesses_is_reactance")

)
pp_check(is_reactance, type='hist')
## Using 10 posterior draws for ppc type 'hist' by default.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

pp_check(is_reactance)
## Using 10 posterior draws for ppc type 'dens_overlay' by default.

try(loo(is_reactance))
## 
## Computed from 12000 by 756 log-likelihood matrix.
## 
##          Estimate   SE
## elpd_loo   -381.8 15.2
## p_loo       340.2 14.1
## looic       763.6 30.5
## ------
## MCSE of elpd_loo is NA.
## MCSE and ESS estimates assume MCMC draws (r_eff in [0.6, 1.2]).
## 
## Pareto k diagnostic values:
##                          Count Pct.    Min. ESS
## (-Inf, 0.7]   (good)       1    0.1%   1672    
##    (0.7, 1]   (bad)      205   27.1%   <NA>    
##    (1, Inf)   (very bad) 550   72.8%   <NA>    
## See help('pareto-k-diagnostic') for details.
plot(is_reactance, ask = FALSE)

summarize_brms(
  is_reactance, 
  model_rows_fixed = model_rows_fixed,
  model_rows_random = model_rows_random,
  model_rownames_fixed = model_rownames_fixed,
  model_rownames_random = model_rownames_random,
  exponentiate = T) %>%
  print_df(rows_to_pack = rows_to_pack)
OR l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 0.01* 0.00 0.52 1.000 4271.62 5789.86
Within-Person Effects
Daily perceived persuasion target -> target 0.40 0.12 1.06 1.000 4382.90 5233.73
Daily perceived persuasion target -> agent 2.67 0.64 14.82 1.001 4001.26 5115.63
Daily perceived pressure target -> target 36.17* 3.45 574.77 1.000 3947.15 5161.33
Daily perceived pressure target -> agent 2.91 0.15 61.16 1.000 5055.42 7063.83
Daily perceived pushing target -> target 4.13* 1.15 21.32 1.000 3980.92 4941.79
Daily perceived pushing target -> agent 0.59 0.09 3.64 1.000 5379.05 6850.20
Day 17.46 0.10 3141.98 1.000 4786.88 6545.99
Daily weartime NA NA NA NA NA NA
Daily perceived support target -> target NA NA NA NA NA NA
Daily perceived support target -> agent NA NA NA NA NA NA
Is a weekend 0.12 0.01 1.46 1.001 4950.94 6614.60
JITAI received 4.53 0.17 146.24 1.000 4576.74 6512.60
Days post skilled support intervention 0.36 0.01 13.76 1.000 4477.98 5635.60
Between-Person Effects
Mean perceived persuasion target -> target 40.75 0.39 7889.80 1.000 4315.16 6354.71
Mean Perceived persuasion target -> agent 9.66 0.07 1430.84 1.000 4912.01 7005.40
Mean Perceived pressure target -> target 17321.32* 23.77 17522651.97 1.001 6426.35 7682.56
Mean Perceived pressure target -> agent 96.36 0.08 109105.44 1.000 5360.49 7202.57
Mean Perceived pushing target -> target 5.76 0.01 6533.77 1.000 6191.67 7824.41
Mean Perceived pushing target -> agent 0.00 0.00 2.80 1.000 6873.54 7429.09
Mean weartime NA NA NA NA NA NA
Difference study group 2 4.17 0.06 334.32 1.001 4645.68 6426.11
Difference study group 3 0.01 0.00 1.37 1.001 4632.38 6735.22
Random Effects
sd(Intercept) 4.42 2.55 6.71 1.00 3776.24 6688.05
sd(Daily perceived persuasion target -> target) 0.89 0.04 2.33 1.00 1824.62 4403.78
sd(Daily perceived persuasion target -> agent) 2.14 0.65 3.93 1.00 3039.73 3385.51
sd(Daily perceived pressure target -> target) 2.08 0.09 5.06 1.00 1914.70 3688.14
sd(Daily perceived pressure target -> agent) 1.48 0.06 4.12 1.00 4524.20 4585.29
sd(Daily perceived pushing target -> target) 1.30 0.11 2.96 1.00 2384.80 3571.83
sd(Daily perceived pushing target -> agent) 0.96 0.03 2.76 1.00 4667.81 5373.75
Additional Parameters
ar[1] 0.07 -0.12 0.26 1.00 1836.22 3172.63
nu NA NA NA NA NA NA
shape NA NA NA NA NA NA
sderr 10.17 6.31 15.09 1.00 2318.65 4389.53
sigma NA NA NA NA NA NA

Report All Models

if (report_ordinal) {
  model_rows_random_final <- model_rows_random_ordinal
  model_rows_fixed_final <- model_rows_fixed_ordinal
  model_rownames_fixed_final <- model_rownames_fixed_ordinal
  model_rownames_random_final <- model_rownames_random_ordinal
  rows_to_pack_final <- rows_to_pack_ordinal
} else {
  model_rows_random_final <- model_rows_random
  model_rows_fixed_final <- model_rows_fixed
  model_rownames_fixed_final <- model_rownames_fixed
  model_rownames_random_final <- model_rownames_random
  rows_to_pack_final <- rows_to_pack
}



all_models <- report_side_by_side(
  pa_sub,
  pa_obj_log,
  mood,
  reactance,
  is_reactance,
  
  model_rows_random = model_rows_random_final,
  model_rows_fixed = model_rows_fixed_final,
  model_rownames_random = model_rownames_random_final,
  model_rownames_fixed = model_rownames_fixed_final
) 
## [1] "pa_sub"
## [1] "pa_obj_log"
## [1] "mood"
## [1] "reactance"
## [1] "is_reactance"
# pretty printing
summary_all_models <- all_models %>%
  print_df(rows_to_pack = rows_to_pack_final) %>%
  add_header_above(
    c(" ", "Subjective MVPA" = 2, 
      "Device-Based MVPA" = 2, 
      "Mood" = 2,
      "Reactance Gaussian" = 2, 
      "Reactance Dichotome" = 2)
  )

export_xlsx(summary_all_models, 
            rows_to_pack = rows_to_pack_final,
            file.path(working_directory, "Output", "NoExchangeProcesses_AllModels_SensCovariates.xlsx"),
            merge_option = 'both', 
            simplify_2nd_row = TRUE,
            colwidths = c(40, 7.4, 12.85, 7.4, 12.85,7.4, 12.85,7.4, 12.85,7.4, 12.85),
            line_above_rows = c(1,2,3,28),
            line_below_rows = c(-1))

summary_all_models
Subjective MVPA
Device-Based MVPA
Mood
Reactance Gaussian
Reactance Dichotome
IRR pa_sub 95% CI pa_sub exp(Est.) pa_obj_log 95% CI pa_obj_log b mood 95% CI mood b reactance 95% CI reactance OR is_reactance 95% CI is_reactance
Intercept 25.01* [16.31, 38.47] 120.79* [100.83, 145.18] 4.64* [ 4.31, 4.97] 0.67* [ 0.42, 0.91] 0.01* [ 0.00, 0.52]
Within-Person Effects
Daily perceived persuasion target -> target 1.20* [ 1.08, 1.35] 1.03 [ 1.00, 1.05] 0.00 [-0.03, 0.03] -0.05 [-0.10, 0.01] 0.40 [ 0.12, 1.06]
Daily perceived persuasion target -> agent 1.18* [ 1.06, 1.33] 1.01 [ 0.99, 1.04] 0.01 [-0.03, 0.05] 0.00 [-0.06, 0.07] 2.67 [ 0.64, 14.82]
Daily perceived pressure target -> target 0.93 [ 0.70, 1.28] 0.95 [ 0.89, 1.01] -0.05 [-0.17, 0.05] 0.25* [ 0.03, 0.47] 36.17* [ 3.45, 574.77]
Daily perceived pressure target -> agent 1.17 [ 0.88, 1.60] 0.98 [ 0.92, 1.04] -0.04 [-0.18, 0.09] 0.14 [-0.06, 0.37] 2.91 [ 0.15, 61.16]
Daily perceived pushing target -> target 1.13 [ 0.92, 1.41] 1.03 [ 0.98, 1.08] 0.02 [-0.04, 0.09] 0.09* [ 0.01, 0.18] 4.13* [ 1.15, 21.32]
Daily perceived pushing target -> agent 1.14 [ 0.97, 1.36] 1.03 [ 0.99, 1.07] 0.06* [ 0.01, 0.12] -0.01 [-0.10, 0.08] 0.59 [ 0.09, 3.64]
Day 0.78 [ 0.51, 1.20] 0.94 [ 0.83, 1.05] 0.21 [-0.01, 0.43] 0.16 [-0.20, 0.53] 17.46 [ 0.10, 3141.98]
Daily weartime NA NA 1.00* [ 1.00, 1.00] NA NA NA NA NA NA
Daily perceived support target -> target NA NA NA NA NA NA NA NA NA NA
Daily perceived support target -> agent NA NA NA NA NA NA NA NA NA NA
Is a weekend 1.26* [ 1.03, 1.53] 1.06* [ 1.02, 1.11] 0.12* [ 0.05, 0.18] -0.14 [-0.30, 0.01] 0.12 [ 0.01, 1.46]
JITAI received 0.78 [ 0.60, 1.02] 0.93* [ 0.88, 0.98] -0.09* [-0.16, -0.01] 0.00 [-0.20, 0.22] 4.53 [ 0.17, 146.24]
Days post skilled support intervention 1.07 [ 0.77, 1.47] 1.04 [ 0.96, 1.14] 0.04 [-0.11, 0.19] -0.10 [-0.36, 0.15] 0.36 [ 0.01, 13.76]
Between-Person Effects
Mean perceived persuasion target -> target 1.64 [ 0.84, 3.21] 1.07 [ 0.79, 1.45] 0.39 [-0.18, 0.94] 0.05 [-0.29, 0.39] 40.75 [ 0.39, 7889.80]
Mean Perceived persuasion target -> agent 1.22 [ 0.64, 2.35] 0.95 [ 0.70, 1.29] 0.30 [-0.26, 0.86] 0.10 [-0.28, 0.48] 9.66 [ 0.07, 1430.84]
Mean Perceived pressure target -> target 0.50 [ 0.23, 1.09] 0.98 [ 0.70, 1.37] -0.30 [-0.88, 0.27] 0.60* [ 0.22, 0.99] 17321.32* [23.77, 17522651.97]
Mean Perceived pressure target -> agent 0.44* [ 0.20, 0.96] 1.00 [ 0.73, 1.38] -0.31 [-0.87, 0.26] 0.19 [-0.23, 0.60] 96.36 [ 0.08, 109105.44]
Mean Perceived pushing target -> target 1.69 [ 0.63, 4.56] 1.03 [ 0.66, 1.61] 0.16 [-0.61, 0.92] -0.20 [-0.70, 0.30] 5.76 [ 0.01, 6533.77]
Mean Perceived pushing target -> agent 1.84 [ 0.68, 5.27] 1.30 [ 0.83, 2.03] 0.30 [-0.46, 1.07] -0.54 [-1.08, 0.00] 0.00 [ 0.00, 2.80]
Mean weartime NA NA 1.00 [ 1.00, 1.00] NA NA NA NA NA NA
Difference study group 2 0.84 [ 0.49, 1.44] 0.89 [ 0.69, 1.15] -0.23 [-0.67, 0.21] 0.08 [-0.19, 0.34] 4.17 [ 0.06, 334.32]
Difference study group 3 1.27 [ 0.74, 2.16] 0.98 [ 0.75, 1.26] 0.37 [-0.10, 0.82] -0.32* [-0.59, -0.04] 0.01 [ 0.00, 1.37]
Random Effects
sd(Intercept) 0.61 [ 0.45, 0.83] 0.30 [0.23, 0.40] 0.55 [0.42, 0.72] 0.19 [ 0.04, 0.36] 4.42 [ 2.55, 6.71]
sd(Daily perceived persuasion target -> target) 0.09 [ 0.00, 0.23] 0.05 [0.03, 0.08] 0.03 [0.00, 0.07] 0.04 [ 0.00, 0.11] 0.89 [ 0.04, 2.33]
sd(Daily perceived persuasion target -> agent) 0.07 [ 0.00, 0.20] 0.05 [0.02, 0.08] 0.06 [0.01, 0.11] 0.05 [ 0.00, 0.13] 2.14 [ 0.65, 3.93]
sd(Daily perceived pressure target -> target) 0.17 [ 0.01, 0.53] 0.05 [0.00, 0.14] 0.12 [0.01, 0.29] 0.40 [ 0.23, 0.62] 2.08 [ 0.09, 5.06]
sd(Daily perceived pressure target -> agent) 0.16 [ 0.01, 0.48] 0.04 [0.00, 0.11] 0.18 [0.02, 0.37] 0.22 [ 0.01, 0.58] 1.48 [ 0.06, 4.12]
sd(Daily perceived pushing target -> target) 0.28 [ 0.02, 0.58] 0.06 [0.00, 0.15] 0.09 [0.01, 0.17] 0.12 [ 0.01, 0.24] 1.30 [ 0.11, 2.96]
sd(Daily perceived pushing target -> agent) 0.11 [ 0.01, 0.31] 0.03 [0.00, 0.08] 0.05 [0.00, 0.13] 0.05 [ 0.00, 0.15] 0.96 [ 0.03, 2.76]
Additional Parameters
ar[1] 0.03 [-0.94, 0.94] 0.30 [0.26, 0.33] 0.45 [0.42, 0.48] 0.01 [-0.08, 0.09] 0.07 [-0.12, 0.26]
nu NA NA NA NA NA NA NA NA NA NA
shape 0.14 [ 0.13, 0.14] NA NA NA NA NA NA NA NA
sderr 0.05 [ 0.00, 0.14] NA NA NA NA NA NA 10.17 [ 6.31, 15.09]
sigma NA NA 0.55 [0.54, 0.57] 0.87 [0.85, 0.89] 0.93 [ 0.88, 0.98] NA NA 
report::report_system()

Analyses were conducted using the R Statistical language (version 4.3.2; R Core Team, 2023) on Windows 11 x64 (build 22635)

report::cite_packages()